Spatial filtering or beamforming is a spatio-temporal technique for wireless communication, speech processing, medical diagnostics, and other applications that require establishing the transmitting direction of target signals in a signal cluttered environment. Human machine interface applications, such as virtual voice assistant using automatic speech recognition (ASR), form the basis of online authentication, access and service management for today's on-the-move users. Such operations require extraction as well as enhancement of speech of the user from noisy signals in the signal cluttered environment, irrespective of the physical location of the user.
Spatial filtering is usually implemented using fixed and adaptive beamforming techniques. The adaptive beamforming technique employs multiple adaptive filters that use estimation errors with fixed and adaptive step size to dynamically update the filtering criteria for removing non-stationary noise elements. However, such adaptive filters converge and diverge for any change in the direction of a target signal as well as switching across the target signal and interference signal during a talk-spurt classification error. Depending upon the step size and changes in surrounding conditions, numerous iterations may be undertaken to achieve a desired degree of clarity of the target signal. Therefore, a dynamic, robust, and adaptive spatial filtering technique is desired that may overcome the aforesaid problems.
Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through comparison of such systems with some aspects of the present disclosure as set forth in the remainder of the present application with reference to the drawings.